Open Source Text Processing Project: Stanford CoreNLP

Deep Learning Specialization on Coursera

Stanford CoreNLP – a suite of core NLP tools

Project Website: http://stanfordnlp.github.io/CoreNLP/

Github Link: https://github.com/stanfordnlp/CoreNLP

Description

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.

Choose Stanford CoreNLP if you need:

An integrated toolkit with a good range of grammatical analysis tools
Fast, reliable analysis of arbitrary texts
The overall highest quality text analytics
Support for a number of major (human) languages
Interfaces available for various major modern programming languages
Stanford CoreNLP is an integrated framework. Its goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. Starting from plain text, you can run all the tools on it with just two lines of code. It is designed to be highly flexible and extensible. With a single option you can change which tools should be enabled and which should be disabled. Stanford CoreNLP integrates many of Stanford’s NLP tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system, sentiment analysis, and the bootstrapped pattern learning tools. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications.


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